[1]}, [2]}. In case when the data are labelled, another way to measure the performance of the autoencoder is by training another neural network using the features from the reduced representation (\(\mathbf {h}\) ) and comparing with performance when trained with original data. In contrast to principal component analysis, autoencoders are capable of nonlinear data reduction and can detect repetitive structures in the data [3]}.
